Recent years have witnessed a phenomenal growth of digitally stored medical information and clinical data which different groups of people may want to access. Healthcare professionals may want to access the information during the process of healthcare planning, decision, and delivery. Patients and other non-healthcare professionals may want to access the information to enrich their knowledge, or to obtain insight on a particular medical condition, its cause and/or treatment.
Assume, for example, a typical clinical environment where a physician tries to find diagnosis or therapy options for a patient's disease based on the patient's past clinical reports and major complaints. In order to efficiently locate the most relevant medical literature, the physician manually forms a short query made up of one or more keywords and submits the query to an information retrieval system. In order for the search to be productive, however, the physician must carefully select the keywords to best summarize the patient's past history and symptoms, and clearly define the physician's specific information needs, e.g. regarding “diagnosis,” “treatment”
Traditional information retrieval systems are inadequate for handling scenario-specific searches as the one described above. This is because such systems often suffer from the fundamental problem of query-document mismatch. The scenario terms in the scenario-specific queries are often general, e.g. “treatment” in the query “lung cancer treatment,” while full-text medical documents often discuss the same topic using much more specialized terms, e.g., “lung excision” or “chemotherapy.” Such general scenario terms fail to match with the specialized terms in relevant documents, resulting in poor retrieval performance. Because of such ineffectiveness, searching online document collections for clinical usage is often frustrating, labor-intensive, and time-consuming.
Accordingly, what is desired is a more efficient and effective system and method for retrieving scenario-specific information.